This article surveys the main philosophical and formal ideas revolving around language as being conventional from the perspective of game theory. For very basic situations, this leads to a coherent view of conventions that offers interesting insights. Although there exist many open problems, this article will argue by outlining partial solution attempts that there is no principled reason for not applying methods from game theory to them.

To coordinate action, information must be transmitted, processed, and utilized to make decisions. Transmission of information requires the existence of a signaling system in which the signals that are exchanged are coordinated with the appropriate content. Signaling systems in nature range from quorum signaling in bacteria [Schauder and Bassler, Kaiser ], through the dance of the bees [Dyer and Seeley ], birdcalls [Hailman, Ficken, and Ficken, Gyger, Marler and Pickert, Evans, Evans, and Marler, Charrier and Sturdy ], and alarm calls (...) in many species [Seyfarth and Cheney, Green and Maegner,Manser, Seyfarth and Cheney ], up to human language. Information processing includes filtering – that is discarding irrelevant information and passing along what is important – and integration of multiple pieces of information. Integration includes logical inference and voting. Finally, the information must be used to make decisions with consequences for payoffs that drive evolution or learning. (shrink)

This paper applies the theoretical criteria laid out by D’Arms et al. to various aspects of evolutionary models of signalling. The question that D’Arms et al. seek to answer can be formulated as follows: Are the models that we use to explain the phenomena in question conceptually adequate? The conceptual adequacy question relates the formal aspects of the model to those aspects of the natural world that the model is supposed to capture. Moreover, this paper extends the analysis of D’Arms (...) et al. by asking the following additional question: Are the models that we use sufficient to explain the phenomena in question? The sufficiency question asks what formal resources are minimally required for the model to get the right results most of the time. (shrink)

The idea that cognition makes use of one or more “languages of thought” remains central to much cognitive-scientific and philosophical theorizing. And yet, virtually no attention has been paid to the question of how a language of thought might evolve in the first place. In this article, I take some steps towards addressing this issue. With the aid of the so-called Sender–Receiver framework, I elucidate a family of distinctions and processes which enable us to see how languages of thought might (...) evolve via a series of small, incremental changes. While much work doubtlessly remains to be done in this area, I hope to at least show that there need be nothing mysterious about the existence of languages of thought on evolutionary grounds. (shrink)

Robustness analysis is widespread in science, but philosophers have struggled to justify its confirmatory power. We provide a positive account of robustness by analysing some explicit and implicit uses of within and across-model robustness in evolutionary theory. We argue that appeals to robustness are usually difficult to justify because they aim to increase the likeliness that a phenomenon obtains. However, we show that robust results are necessary for explanations of phenomena with specific properties. Across-model robustness is necessary for how-possibly explanations (...) of multiply instantiated phenomena, while within-model robustness is necessary for explanations of phenomena with multiple evolutionary starting points. In such cases, the appeal of robustness is explanatory rather than confirmatory. (shrink)

It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..